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Edona Elshan

Wissenschaftliche Mitarbeiterin
+41 71 224 3304


Intelligent agents (IAs) are permeating both business and society. However, interacting with IAs poses challenges moving beyond technological limitations towards the human-computer interface. Thus, the knowledgebase related to interaction with IAs has grown exponentially but remains segregated and impedes the advancement of the field. Therefore, we conduct a systematic literature review to integrate empirical knowledge on user interaction with IAs. This is the first paper to examine 107 Information Systems and Human-Computer Interaction papers and identified 389 relationships between design elements and user acceptance of IAs. Along the independent and dependent variables of these relationships, we span a research space model encompassing empirical research on designing for IA user acceptance. Further we contribute to theory, by presenting a research agenda along the dimensions of the research space, which shall be useful to both researchers and practitioners. This complements the past and present knowledge on designing for IA user acceptance with potential pathways into the future of IAs.

The role of the Chief Information Officer (CIO) in the organization has received a lot of attention in recent years. While traditionally most CIOs had faced the difficulty of stepping out of the shadow of being coined as a "Utility & Infrastructure Director", they have been found to establish themselves as a driving force in defining and shaping the digital agenda and strategic direction of their organization (Peppard et al. 2011). This crisis-driven development is, however, now paving the way for a new era of the CIO role. As research shows, crises usually do not lead to a trend reversal, but to a trend acceleration (Gassmann and Ferrandina 2021). Therefore, this opportunity should be seized by CIOs in order to leverage the digitalization momentum gained through the COVID-19 crisis, and to build lean digital organizational structures and use strategic sourcing of services for cost efficiency. Thus, the focus here should not be on rebuilding old barriers, but to use the crisis induced dynamic to empower the CIO to successfully master the future challenges of efficiency, flexibility, resilience, scalability and innovation in the organization.

Since the emergence of conversational agents, this technology has seen continuous development and research. Today, advanced conversational agents are virtually omnipresent in our everyday lives. Albeit the numerous improvements in their conversational capabilities, breakdowns are still a persistent issue. Such breakdowns can result in a very unpleasant experience for users and impair the future success of conversational agents. This issue has been acknowledged by many researchers recently. However, the research on strategies to overcome conversational breakdowns is still inconclusive, and further research is needed. Therefore, we conduct a systematic literature analysis to derive conceptual conversational breakdown recovery strategies from literature and highlight future research avenues to address potential gaps. Thus, we contribute to theory of human-agent interaction by deriving and assessing recovery strategies and suggesting leads for novel recovery strategies.

Facilitated by Artificial Intelligence technology, cognitive automation means to front and back offices what the pervasive automation through physical machinery and robots meant to production plants. Thus, we can automate tasks and processes that were unimaginable to be automated one decade ago. However, organizational adoption of cognitive automation is way below its possibilities, as this novel class of automation technology is perceived to be risky by organizations. This demands structured approaches for assessing the suitability of use cases for cognitive automation. Following the Design Science Research paradigm, we develop a method for assessing cognitive automation use cases. This enables practitioners to make more informed decisions on selecting, specifying, and embedding cognitive automation use cases in their organizations. For researchers, the method serves as a conceptual frame, which they can adapt to guide their empirical research or to use it for developing future decision support to shape the future of work.

The ever-increasing complexity of the music industry and the intensified resentment of artists towards collecting societies call for a transformation and a change of behavior within the music ecosystem. This article introduces a hybrid intelligence system, that ameliorates the current situation by combining the intelligence of humans and machines. This study proposes design requirements for hybrid intelligence systems in the music industry. Using a design science research approach, we identify design requirements both inductively from expert interviews and deductively from theory and present a first prototypical instantiation of a respective hybrid intelligence system. Overall, this shall enrich the body of knowledge of hybrid intelligence research by transferring its concepts into a new context. Furthermore, the identified design requirements shall serve as a foundation for researchers and practitioners to further explore and design hybrid intelligence in the music industry and beyond.

The success of projects is, amongst others, highly depended on the team members skillset and ability to collaborate. Hence, education has to undergo a change to keep up with the shift in the compositions of skills and knowledge needed for students. To overcome scalability issues, we propose to develop Timmy-a conversational agents (CAs) that acts as a team member. As there is a lack of concrete design knowledge concerning CAs as peers in teams, we conduct a design science research project. Based on requirements from scientific literature and expert interviews, we develop a concise set of design principles for designing CAs in peer roles in educational settings. Furthermore, we present an initial proof of concept evaluation. These insights will support researchers and practitioners to understand better how CAs can be systematically built to ameliorate the collaborative skill of students in teamwork settings.

Cognitive automation moves beyond rule-based automation and thus imposes novel challenges on organizations when assessing the automation potential of use cases. Thus, we present an empirically grounded and conceptually operationalized model for assessing cognitive automation use cases, which consists of four assessment dimensions: data, cognition, relationship, and transparency requirements. We apply the model in a real-world organizational context in the course of an action research project at the customer service department of ManuFact AG, and present unique empirical insights as well as the impact the application of the model had on the organization. The model shall help practitioners to make more informed decisions on selecting use cases for cognitive automation and to plan respective endeavors. For research, the identified factors affecting the suitability of a use case for cognitive automation shall deepen our understanding of cognitive automation in particular, and AI as the driving force behind cognitive automation in general.

Information technology capabilities are growing at an impressive pace and increasingly overstrain the cognitive abilities of users. User assistance systems such as online manuals try to help the user in handling these systems. However, there is strong evidence that traditional user assistance systems are not as effective as intended. With the rise of smart personal assistants, such as Amazon’s Alexa, user assistance systems are becoming more sophisticated by offering a higher degree of interaction and intelligence. This study proposes a process model to develop Smart Personal Assistants. Using a design science research approach, we first gather requirements from Smart Personal Assistant designers and theory, and later evaluate the process model with developing an Amazon Alexa Skill for a Smart Home system. This paper contributes to the existing user assistance literature by offering a new process model on how to design Smart Personal Assistants for intelligent systems.

The knowledge base related to user interaction with conversational agents (CAs) has grown dramatically but remains segregated. In this paper, we conduct a systematic literature review to investigate user interaction with CAs. We examined 107 papers published in outlets related to IS and HCI research. Then, we coded for design elements and user interaction outcomes, and isolated 7 significant determinants of these outcomes, as well as 42 themes with inconsistent evidence, providing grounds for future research. Building upon the insights from the analysis, we propose a research agenda to guide future research surrounding user interaction with CAs. Ultimately, we aim to contribute to the body of knowledge of IS and HCI in general and user interaction with CA in particular by indicating how developed a research field is regarding the number and content of the respective contributions. Furthermore, practitioners benefit from a structured overview related to CA design effects.